The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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This paper proposes a distributed algorithm to detect outliers for large and distributed datasets. The algorithm employs the basis of distance-based outliers based on the distance of a point to its kth nearest neighbor. It declares the top n points in the ranking to be outliers. To the best of our knowledge, this is the first proposal of a distributed algorithm for outlier detection for shared-nothing multiple processor computing environments. It has four phases. First, in each processing node, the algorithm partitions the input data set into disjoint subsets, then it prunes entire partitions as soon as it is determined that they cannot contain outliers. Then it applies a global filtering technique to collect the partitions as global candidates from local candidate partitions in each processing node. Further, it introduces a load balancing algorithm to balance the number of local candidate partitions. Finally, it identifies outliers from each processing node.